Automated identification system for seizure EEG signals using tunable-Q wavelet transform

Abstract In the present work, EEG signals of different classes are analysed in tunable-Q wavelet transform (TQWT) framework. The TQWT decomposes the EEG signals into subbands and arrange them into decreasing order of frequency. The nonlinearity of the EEG signals is assessed by computing the centered correntropy (CCE) from the obtained subbands, which is further used as a feature for classifying the different categories of EEG signals. In this work, EEG signals are categorised in two different classification problems. First category is seizure free and seizure (NF-S) classes, and the other one is the normal, seizure free and seizure (ZO-NF-S) classes. Features obtained from the EEG signals of these classes are fed to the input of three different classifiers namely, random forest classifier (RF), multilayer perceptron (MLP) classifier, and logistic regression (LR) classifier. For NF-S classes, we achieved 98.3% classification accuracy with RF classifier for signal length of 1000 samples. The obtained accuracy of classification is 98.2% for ZO-NF-S classes using MLP classifier when features are extracted from signal length of 1000 samples.

[1]  Yunmei Chen,et al.  A test of independence based on a generalized correlation function , 2011, Signal Process..

[2]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[3]  Ram Bilas Pachori,et al.  Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition , 2008, J. Electr. Comput. Eng..

[4]  Abdulhamit Subasi,et al.  Classification of EEG signals using neural network and logistic regression , 2005, Comput. Methods Programs Biomed..

[5]  Ram Bilas Pachori,et al.  Classification of ictal and seizure-free EEG signals using fractional linear prediction , 2014, Biomed. Signal Process. Control..

[6]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[7]  Raksha Upadhyay,et al.  A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform , 2016, Comput. Electr. Eng..

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Bijaya K. Panigrahi,et al.  Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals , 2017, IEEE Journal of Biomedical and Health Informatics.

[10]  Yi Chai,et al.  Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM , 2014, Biomed. Signal Process. Control..

[11]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[12]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[13]  Rajeev Sharma,et al.  Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions , 2015, Expert Syst. Appl..

[14]  R. B. Pachori,et al.  Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals , 2017 .

[15]  Josemir W Sander,et al.  Standards for epidemiologic studies and surveillance of epilepsy , 2011, Epilepsia.

[16]  Ram Bilas Pachori,et al.  Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions , 2014, Comput. Methods Programs Biomed..

[17]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[18]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[19]  Joan Fisher Box,et al.  Guinness, Gosset, Fisher, and Small Samples , 1987 .

[20]  Dimitrios I. Fotiadis,et al.  Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks , 2007, Comput. Intell. Neurosci..

[21]  Pere Caminal,et al.  Correntropy measures to detect daytime sleepiness from EEG signals , 2014, Physiological measurement.

[22]  Ram Bilas Pachori,et al.  Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals , 2018 .

[23]  Musa Peker,et al.  A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers , 2016, IEEE Journal of Biomedical and Health Informatics.

[24]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[25]  José Carlos Príncipe,et al.  Generalized correlation function: definition, properties, and application to blind equalization , 2006, IEEE Transactions on Signal Processing.

[26]  B. Aazhang,et al.  An algorithm for training multilayer perceptrons for data classification and function interpolation , 1994 .

[27]  U. Rajendra Acharya,et al.  Decision support system for focal EEG signals using tunable-Q wavelet transform , 2017, J. Comput. Sci..

[28]  Osman Erogul,et al.  Epileptic EEG detection using the linear prediction error energy , 2010, Expert Syst. Appl..

[29]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[30]  Sandipan Pati,et al.  Pharmacoresistant epilepsy: From pathogenesis to current and emerging therapies , 2010, Cleveland Clinic Journal of Medicine.

[31]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[32]  Natarajan Sriraam,et al.  Context-based near-lossless compression of EEG signals using neural network predictors , 2009 .

[33]  Ivan W. Selesnick,et al.  Wavelet Transform With Tunable Q-Factor , 2011, IEEE Transactions on Signal Processing.

[34]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Boualem Boashash,et al.  Time-Frequency Signal Analysis and Processing: A Comprehensive Reference , 2015 .

[36]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[37]  V. Srinivasan,et al.  Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features , 2005, Journal of Medical Systems.